In this paper, we propose a novel method to measure the distance between two Gaussian Mixture Models. The proposed distance measure is based on the minimum cost that must paid to transform from one Gaussian Mixture Mo...
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ISBN:
(纸本)9781605588407
In this paper, we propose a novel method to measure the distance between two Gaussian Mixture Models. The proposed distance measure is based on the minimum cost that must paid to transform from one Gaussian Mixture Model into the other. We parameterize the components of a Gaussian Mixture Model which are Gaussian probability density functions (pdf) as positive definite lower triangular transformation matrices. Then we identify that Gaussian pdfs form a Lie group. Based on Lie group theory, the geodesic length can be used to measure the minimum cost that must paid to transform from one Gaussian pdf into the other. Combining geodesic length with the earth mover's distance, we propose the Lie group earth mover's distance for Gaussian Mixture Models. We test our distance measure in image retrieval. The experimental results indicate that our distance measure is more effective than other measures including the Kullback-Liebler divergence. Copyright 2009 ACM.
The inter-domain role mapping is a basic method for facilitating interoperation in RBAC-based collaborating environments, where each domain employs role based access control (RBAC) to specify access control policies. ...
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The inter-domain role mapping is a basic method for facilitating interoperation in RBAC-based collaborating environments, where each domain employs role based access control (RBAC) to specify access control policies. Prior to concrete interoperation, one important problem is to establish role mappings. Two issues are involved in the establishing process. The first one is to generate role mappings while respecting RBAC states such as separation of duty (SoD) constraints. On the other hand, administrative works of RBAC policies are sometimes needed to generate mappings. This paper investigates these two problems, mostly from the computational perspective. In particular, we study how to find a set of roles appropriate for mappings and how to fulfill interoperation requests; it turns out that most of corresponding problems are NP-complete. Further, several useful subcases of these problems are identified. We also motivate and support partial interoperation by imposing constraints on interoperation requests. When administrative works are necessary, we examine how to minimize administrative cost; the result is that one subcase of the problem reduces to the "minimal set cover" (MSC) problem. We demonstrate that approaches to MSC can be applied to this problem, even though they are not totally equivalent. Finally, a discussion on how administrative operations made to RBAC states may influence interoperability is presented as well.
This paper introduces a feature descriptor called shape of Gaussian (SOG), which is based on a general feature descriptor design framework called shape of signal probability density function (SOSPDF). SOSPDF takes the...
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This paper introduces a feature descriptor called shape of Gaussian (SOG), which is based on a general feature descriptor design framework called shape of signal probability density function (SOSPDF). SOSPDF takes the shape of a signal's probability density function (pdf) as its feature. Under such a view, both histogram and region covariance often used in computer vision are SOSPDF features. Histogram describes SOSPDF by a discrete approximation way. Region covariance describes SOSPDF as an incomplete parameterized multivariate Gaussian distribution. Our proposed SOG descriptor is a full parameterized Gaussian, so it has all the advantages of region covariance and is more effective. Furthermore, we identify that SOGs form a Lie group. Based on Lie group theory, we propose a distance metric for SOG. We test SOG features in tracking problem. Experiments show better tracking results compared with region covariance. Moreover, experiment results indicate that SOG features attempt to harvest more useful information and are less sensitive against noise.
Detecting human in still images is one of the most challenging object detection problems. In this paper we apply the scale theory to human detection. By integrating Gaussian Pyramids multi-scale object representation ...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
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